Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 July 2026 has been entered
Response to Arguments
Applicant’s arguments with respect to claims 1-30 have been considered but are moot because the arguments do not apply to any of the references being used in the current rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless -
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 7-13, 16, 20-23, 28, and 29 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Jain et al. (US 2024/0007414, hereinafter Jain).
Regarding claim 1, Jain discloses
A method comprising (fig. 1-24):
using one or more machine learning models to output one or more predicted actions to modify at least one current state of a computing system (paragraph [0226]: the example agent managing circuitry ID5_C310 invokes a workload agent to be associated and/or otherwise assigned with the workload evaluation … the assigned workload agent is a reinforcement learning agent; paragraph [0229]: the example reconfiguration managing circuitry ID5_C308 selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance; paragraph [0230]: subsequently moves the affected workload(s) to alternate computing resources to complete workload objectives; Note: The “reinforcement learning agent” corresponds to the claimed machine learning model. Selecting an “alternate path (e.g., alternate graph)” to move workloads to alternate computing resources corresponds to outputting a predicted action used to modify the current state of the computing system), wherein the predicted actions are generated from one or more first inputs to the one or more machine learning models, one or more second inputs to the one or more machine learning models, and at least one desired state of the computing system (paragraph [0220]: Examples disclosed herein retrieve, receive and/or otherwise obtain SLA information/parameters ID5_A114 and current utilization information ID5_A116; paragraph [0223]: trigger dynamic decisions of (a) candidate target computing devices to best handle the workload and (b) candidate graphs to best facilitate workload operation in view of dynamic SLA information, dynamic computing resource conditions, and key performance indicator (KPI) information; paragraph [0229]: exhibits predicted SLA compliance to a threshold margin; Note: The “current utilization information” and “dynamic computing resource conditions” correspond to the first inputs. The “SLA information/parameters” correspond to the second inputs. The “predicted SLA compliance to a threshold margin” corresponds to the desired state of the computing system), the one or more first inputs being based, at least in part, on one or more values of at least one operating parameter of the computing system (paragraph [0228]: The example reconfiguration managing circuitry ID5_C308 retrieves current utilization information for the computing resources associated with the above-identified workload invocation … Resource information may include, but is not limited to resource availability, current resource utilization; Note: Resource availability and current resource utilization are operating parameters of the computing system) and the one or more second inputs being based, at least in part, on one or more values of at least one system objective (paragraph [0220]: As used herein, SLA parameters represent constraints to be satisfied by workload execution, such as accuracy metrics, speed metrics, power consumption metrics, cost (e.g., financial cost, processor burden cost, etc.) metrics, etc.; Note: The SLA parameters such as accuracy, speed, and power consumption metrics that must be satisfied correspond to the claimed system objectives); and
causing an application, which receives the one or more predicted actions output from the one or more machine learning models, to implement the one or more predicted actions to modify the current state of the computing system to a different state of the computing system (paragraph [0229]: the example reconfiguration managing circuitry ID5_C308 selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance to a threshold margin; paragraph [0230]: the benchmark managing circuitry ID5_C302… subsequently moves the affected workload(s) to alternate computing resources to complete workload objectives; Note: The reconfiguration managing circuitry and benchmark managing circuitry act as the application that receives the selected alternate path (predicted action) and implements it by moving the workloads to alternate resources, thereby modifying the current state of the computing system to a different state).
Regarding claim 4, Jain discloses
wherein the first inputs comprise one or more first embeddings generated using the one or more values of the at least one operating parameter of the computing system, and the second inputs comprise one or more second embeddings generated using the one or more values of the at least one system objective (paragraph [0220]: embed additional semantic information into the workload so that on-the-fly decisions can occur in view of dynamic conditions during runtime ID5_A112. Examples disclosed herein retrieve, receive and/or otherwise obtain SLA information/parameters ID5_A114 and current utilization information ID5_A116; paragraph [0223]: Different embeddings capture corresponding probabilities of certain paths in a graph… incorporate semantic information that can trigger dynamic decisions of (a) candidate target computing devices to best handle the workload and (b) candidate graphs to best facilitate workload operation in view of dynamic SLA information, dynamic computing resource conditions, and key performance indicator (KPI) information; Note: Jain explicitly teaches using different embeddings that incorporate the dynamic computing resource conditions (first embeddings based on operating parameters) and the dynamic SLA information (second embeddings based on system objectives) to trigger the dynamic decisions).
Regarding claim 7, Jain discloses
further comprising: performing at least one workload on the computing system, the at least one system objective being associated with the at least one workload (paragraph [0219]: the framework ID5_A100 includes an example workload or pool of workloads ID5_A102 and example platform resources ID5_A104 that are known to be available at a given moment. The example platform resources ID5_A104 include any number and or type of devices capable of performing workload tasks; paragraph [0220]: Examples disclosed herein retrieve, receive and/or otherwise obtain SLA information/parameters ID5_A114 and current utilization information ID5_A116. As used herein, SLA parameters represent constraints to be satisfied by workload execution, such as accuracy metrics, speed metrics, power consumption metrics, cost; Note: Jain teaches performing a workload on computing system platform resources, where the SLA parameters, which correspond to the system objectives, are associated with the execution of the workload).
Regarding claim 8, Jain discloses
further comprising: occasionally repeating selecting the one or more predicted actions, and causing the application to implement the one or more predicted actions (paragraph [0313]: the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model; paragraph [0314]: if the system flow of FIG. ID3_6 is a closed-loop process, the controller circuit ID3_420 collects actual resource utilization data and generates subsequent candidate models for the AI models ID3_404a-c in a recurring fashion. In this examples, the controller circuit ID3_420 may analyze actual resource utilization data and performance data associated with previously applied candidate models to analyze and select subsequent candidate models to move closer to a desired performance; Note: Jain teaches repeating the selection of subsequent candidate models, which correspond to the predicted actions, in a recurring fashion and causing the system to implement the selected actions by setting the cache size and memory bandwidth resources per the selected model).
Regarding claim 9, Jain discloses
further comprising: using at least one of machine learning or artificial intelligence to obtain the at least one current state of the computing system (paragraph [0602]: during respective iterations, the example quantization controller circuitry ID7_102 obtains the current state from the previous iteration and the performance metrics, such as accuracy, size, and latency, for the current state st … The example quantization controller circuitry ID7_102 determines this through various convergence strategies including, but not limited to, reinforcement learning (e.g., policy gradient), evolutionary algorithms (e.g., genetic algorithms), and heuristic search; Note: Jain teaches using artificial intelligence/machine learning techniques, such as reinforcement learning, to obtain and evaluate the current state and performance metrics of the computing system).
Regarding claim 10, Jain discloses
wherein the one or more predicted actions comprise at least one of modifying a number of workloads being performed by the computing system or modifying hardware resources of the computing system (paragraph [0313]: At block ID3_616, the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model. The example monitor circuit ID3_424 may instruct the cache ID3_412 and/or the memory ID3_416 to commit an amount of space and/or bandwidth, respectively, to one or more AI models; Note: Jain teaches setting and committing cache size and memory bandwidth resources, which corresponds to the predicted actions comprising modifying hardware resources of the computing system).
Regarding claim 11, Jain discloses
A system comprising (fig. 1-24):
one or more hardware resources; a processing environment comprising at least a portion of the one or more hardware resources; and circuitry to (paragraph [0105]: the compute node D100 includes a compute engine (also referred to herein as ‘compute circuitry’) D102, an input/output (I/O) subsystem D108, data storage D110; paragraph [0219]: the framework ID5_A100 includes an example workload or pool of workloads ID5_A102 and example platform resources ID5_A104 that are known to be available at a given moment. The example platform resources ID5_A104 include any number and or type of devices capable of performing workload tasks):
obtain one or more values of one or more parameters as one or more workloads are performed by the processing environment in a current state (paragraph [0228]: During a runtime phase, the example workload activity detecting circuitry ID5_C312 monitors a platform for whether a labelled workload … has been invoked; paragraph [0307]: the example monitor ID3_424 (FIG. 1) collects resource utilization data based on the running AI models ID3_404a-c. For example, the collected resource utilization data may include, but is not limited to, the space utilization of the cache ID3_412, the space utilization of the memory ID3_416, and/or the bandwidth utilization);
use one or more machine learning models to output at least one predicted action based at least in part on one or more first inputs and one or more second inputs, the one or more first inputs to be based at least in part on the one or more values of the one or more parameters, the one or more second inputs to be based at least in part on one or more values of one or more objectives associated with the one or more workloads (paragraph [0227]: The example hyper parameter tuning circuitry ID5_C306 calculates an optimized graph for the selected resource. In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information; Note: The reinforcement learning model uses utilization parameters as first inputs and SLA parameters/objectives as second inputs to output predicted actions/graphs), outputting the at least one predicted action to comprise: generating one or more predicted actions based at least in part on the first inputs and the second inputs (paragraph [0308]: the example analyzer circuit ID3_428 (FIG. 1) generates candidate models based on the collected resource utilization data; paragraph [0229]: the example reconfiguration managing circuitry ID5_C308 selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance);
predicting sets of one or more different future states of the processing environment if the one or more predicted actions are taken with respect to the processing environment (paragraph [0294]: the example analyzer circuit ID3_428 generates subsequent candidate models and corresponding expected resource utilization data to continue analyzing QoS levels; paragraph [0523]: The value network outputer ID6_526 predicts the value of the skilled agent when it is in a given state. In some examples the value network outputer ID6_526 predicts the value of the action the skilled agent can take); and
determining a selected set of the sets that more closely matches at least one desired state of the processing environment than at least one other of the sets, the selected set having been predicted for the at least one predicted action of the one or more predicted actions (paragraph [0229]: selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance to a threshold margin; paragraph [0294]: In this manner, the analyzer circuit ID3_428 can select a subsequent candidate model that will more closely satisfy the cache space and memory bandwidth needs of the AI models ID3_404a-c to replace the currently running AI model); and
perform the at least one predicted action to modify the current state of the processing environment to a different state of the processing environment (paragraph [0313]: At block ID3_616, the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model. The example monitor circuit ID3_424 may instruct the cache ID3_412 and/or the memory ID3_416 to commit an amount of space and/or bandwidth, respectively, to one or more AI models).
Regarding claim 12, Jain discloses
wherein the circuitry is to instruct the processing environment to perform the one or more workloads (paragraph [0336]: At block ID3_726, the example orchestrator circuit ID3_104 instantiates the workload (e.g., spin-up the requested instance) requested by the tenant ID3_302; paragraph [0344]: At block ID3_818, the example computing node ID3_400 executes AI models ID3_404a-c. For example, the AI models ID3_404a-c executes using the resources based on the at least one selected resource utilization models; Note: Jain teaches the circuitry, such as the orchestrator circuit, instructing the processing environment to instantiate and execute the requested workloads/AI models).
Regarding claim 13, Jain discloses
wherein the at least one predicted action comprises at least one of modifying the one or more workloads being performed by the processing environment or modifying the portion of the one or more hardware resources of the processing environment (paragraph [0313]: At block ID3_616, the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model. The example monitor circuit ID3_424 may instruct the cache ID3_412 and/or the memory ID3_416 to commit an amount of space and/or bandwidth, respectively, to one or more AI models; paragraph [0288]: The example workload migrator circuit ID3_318 is to determine to migrate (e.g., relocate, move) a workload from an example first node ID3_304 to an example second node ID3_306; Note: Jain teaches both modifying the hardware resources by committing specific amounts of cache/memory bandwidth to models, as well as modifying the workloads being performed by migrating them between nodes).
Regarding claim 16, Jain discloses
wherein the one or more first inputs comprise one or more first embeddings generated using the one or more values of the one or more parameters, and the one or more second inputs comprise one or more second embeddings generated using the one or more values of the one or more objectives (paragraph [0220]: embed additional semantic information into the workload so that on-the-fly decisions can occur in view of dynamic conditions during runtime ID5_A112. Examples disclosed herein retrieve, receive and/or otherwise obtain SLA information/parameters ID5_A114 and current utilization information ID5_A116; paragraph [0223]: Different embeddings capture corresponding probabilities of certain paths in a graph… incorporate semantic information that can trigger dynamic decisions of (a) candidate target computing devices to best handle the workload and (b) candidate graphs to best facilitate workload operation in view of dynamic SLA information, dynamic computing resource conditions, and key performance indicator (KPI) information; Note: Jain explicitly teaches using different embeddings that incorporate the dynamic computing resource conditions (first embeddings based on operating parameters) and the dynamic SLA information (second embeddings based on system objectives) to trigger the dynamic decisions).
Regarding claim 20, Jain discloses
wherein reinforcement learning is used to select the at least one predicted action (paragraph [0227]: In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information; paragraph [0602]: The example quantization controller circuitry ID7_102 determines this through various convergence strategies including, but not limited to, reinforcement learning (e.g., policy gradient), evolutionary algorithms (e.g., genetic algorithms), and heuristic search; Note: Jain explicitly teaches using a reinforcement learning model to evaluate parameters and select the optimal actions/configurations for the computing system).
Regarding claim 21, Jain discloses
wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a first system for performing simulation operations; a second system for performing deep learning operations; a third system implemented using an edge device; a fourth system implemented using a robot; a fifth system incorporating one or more virtual machines (VMs); a sixth system implemented at least partially in a data center; a seventh system for performing digital twin operations; an eighth system for performing light transport simulation; a nineth system for performing collaborative content creation for 3D assets; a tenth system for performing conversational Artificial Intelligence operations; an eleventh system for generating synthetic data; a twelfth system for implementing a web-hosted service for detecting program workload inefficiencies; an application as an application programming interface ("API"); a thirteenth system implemented at least partially using cloud computing resources; or a fourteenth system for presenting one or more of virtual reality content, augmented reality content, or mixed reality content (paragraph [0288]: The example workload migrator circuit ID3_318 is to determine to migrate (e.g., relocate, move) a workload from an example first node ID3_304 to an example second node ID3_306; paragraph [0336]: the example orchestrator circuit ID3_104 instantiates the workload (e.g., spin-up the requested instance) requested by the tenant ID3_302; Note: Jain’s system of orchestrating workloads, spinning up instances for tenants, and migrating workloads between compute nodes describes a system implemented using cloud computing resources, virtual machines, and/or a data center environment).
Regarding claim 22, Jain discloses
One or more processors comprising (fig. 1-24): circuitry to (paragraph [0105]: the compute node D100 includes a compute engine (also referred to herein as ‘compute circuitry’) D102, an input/output (I/O) subsystem D108):
obtain one or more values of one or more parameters as one or more workloads are performed by a processing environment in a current state (paragraph [0228]: During a runtime phase, the example workload activity detecting circuitry ID5_C312 monitors a platform for whether a labelled workload … has been invoked; paragraph [0307]: the example monitor ID3_424 (FIG. 1) collects resource utilization data based on the running AI models ID3_404a-c);
use one or more machine learning models to output at least one predicted action based on first inputs and second inputs, the first inputs to be based at least in part on the values of the one or more parameters, the second inputs to be based at least in part on values of one or more objectives associated with the one or more workloads (paragraph [0227]: The example hyper parameter tuning circuitry ID5_C306 calculates an optimized graph for the selected resource. In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information), outputting the at least one predicted action to comprise: causing the one or more machine learning models to receive the first inputs and the second inputs and, in response, generate one or more predicted actions (paragraph [0308]: the example analyzer circuit ID3_428 (FIG. 1) generates candidate models based on the collected resource utilization data; paragraph [0229]: the example reconfiguration managing circuitry ID5_C308 selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance);
selecting a selected set from sets of one or more different future states predicted using the one or more predicted actions, the selected set to more closely match at least one desired state of the processing environment than at least one other of the sets, the selected set corresponding to the at least one predicted action (paragraph [0229]: selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance to a threshold margin; paragraph [0294]: the example analyzer circuit ID3_428 generates subsequent candidate models and corresponding expected resource utilization data to continue analyzing QoS levels … In this manner, the analyzer circuit ID3_428 can select a subsequent candidate model that will more closely satisfy the cache space and memory bandwidth needs of the AI models ID3_404a-c to replace the currently running AI model); and
cause the at least one predicted action to be performed to modify the current state of the processing environment to a different state of the processing environment (paragraph [0313]: At block ID3_616, the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model. The example monitor circuit ID3_424 may instruct the cache ID3_412 and/or the memory ID3_416 to commit an amount of space and/or bandwidth, respectively, to one or more AI models).
Regarding claim 23, Jain discloses
wherein the at least one predicted action comprises at least one of modifying the one or more workloads being performed by the processing environment or modifying one or more hardware resources of the processing environment (paragraph [0313]: At block ID3_616, the example monitor circuit ID3_424 sets the cache size and/or memory bandwidth resources per the selected candidate model. The example monitor circuit ID3_424 may instruct the cache ID3_412 and/or the memory ID3_416 to commit an amount of space and/or bandwidth, respectively, to one or more AI models; paragraph [0288]: The example workload migrator circuit ID3_318 is to determine to migrate (e.g., relocate, move) a workload from an example first node ID3_304 to an example second node ID3_306; Note: Jain teaches both modifying the hardware resources by committing specific amounts of cache/memory bandwidth to models, as well as modifying the workloads being performed by migrating them between nodes).
Regarding claim 28, Jain discloses
wherein the first inputs comprise one or more first embeddings generated using the one or more values of the one or more parameters, and the second inputs comprise one or more second embeddings generated using the one or more values of the one or more objectives (paragraph [0220]: embed additional semantic information into the workload so that on-the-fly decisions can occur in view of dynamic conditions during runtime ID5_A112. Examples disclosed herein retrieve, receive and/or otherwise obtain SLA information/parameters ID5_A114 and current utilization information ID5_A116; paragraph [0223]: Different embeddings capture corresponding probabilities of certain paths in a graph… incorporate semantic information that can trigger dynamic decisions of (a) candidate target computing devices to best handle the workload and (b) candidate graphs to best facilitate workload operation in view of dynamic SLA information, dynamic computing resource conditions, and key performance indicator (KPI) information; Note: Jain explicitly teaches using different embeddings that incorporate the dynamic computing resource conditions (first embeddings based on operating parameters) and the dynamic SLA information (second embeddings based on system objectives) to trigger the dynamic decisions).
Regarding claim 29, Jain discloses
wherein the one or more processors are comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a first system for performing simulation operations; a second system for performing deep learning operations; a third system implemented using an edge device; a fourth system implemented using a robot; a fifth system incorporating one or more virtual machines (VMs); a sixth system implemented at least partially in a data center; a seventh system for performing digital twin operations; an eighth system for performing light transport simulation; a nineth system for performing collaborative content creation for 3D assets; a tenth system for performing conversational Artificial Intelligence operations; an eleventh system for generating synthetic data; a twelfth system for implementing a web-hosted service for detecting program workload inefficiencies; an application as an application programming interface ("API"); a thirteenth system implemented at least partially using cloud computing resources; or a fourteenth system for presenting one or more of virtual reality content, augmented reality content, or mixed reality content (paragraph [0288]: The example workload migrator circuit ID3_318 is to determine to migrate (e.g., relocate, move) a workload from an example first node ID3_304 to an example second node ID3_306; paragraph [0336]: the example orchestrator circuit ID3_104 instantiates the workload (e.g., spin-up the requested instance) requested by the tenant ID3_302; Note: Jain’s system of orchestrating workloads, spinning up instances for tenants, and migrating workloads between compute nodes describes a system implemented using cloud computing resources, virtual machines, and/or a data center environment).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made
Claims 2, 3, and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 2024/0007414, hereinafter Jain) in view of Chitalwala et al. (US 2023/0043579, hereinafter Chitalwala).
Regarding claim 2, Jain does not disclose wherein using the one or more machine learning models to output the one or more predicted actions further comprises obtaining one or more metrics that indicate at least one relationship between the values of the at least one operating parameter of the computing system and the values of the at least one system objective, and using the one or more metrics to select the one or more predicted actions. Chitalwala discloses wherein using the one or more machine learning models to output the one or more predicted actions further comprises (fig. 1-6): obtaining one or more metrics that indicate at least one relationship between the one or more values of the at least one operating parameter of the computing system and the one or more values of the at least one system objective (paragraph [0030]: The JLO logic 300 may predict a first performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization; paragraph [0036]: The maximum of the performance metric OER may indicate the optimal job load for the computing application where both the optimal job concurrency and the optimal resource utilization are achieved; Note: Chitalwala teaches obtaining a performance metric, OER, that relates operating parameters such as job load and concurrency to the system objective of optimal resource utilization), and using the one or more metrics to select the one or more predicted actions (paragraph [0049]: In this case, the JLO logic 300 may trigger an alert to rightsize computing resources allocated for use by the computing application … the JLO logic 300 may trigger to upsize the computing resources allocated for use by the computing application; Note: Triggering an alert to rightsize or upsize computing resources based on the evaluated metrics corresponds to using the metrics to select predicted action). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Jain by incorporating Chitalwala’s metric-based action selection. The motivation would have been to monitor and optimize computing resource usage of a cloud-based computing application (Chitalwala, paragraph [0003]) by utilizing a calculated performance metric that explicitly relates current resource utilization parameters to SLA objectives to more accurately trigger dynamic resource allocation and workload routing decisions.
Regarding claim 3, Jaindoes not disclose wherein the one or more metrics comprise one or more cross-correlations between the one or more values of the at least one operating parameter and the one or more values of the at least one system objective. Chitalwala discloses wherein the one or more metrics comprise one or more cross-correlations between the one or more values of the at least one operating parameter and the one or more values of the at least one system objective (aragraph [0013]: FIG. 4 is a chart schematically illustrating an example correlation of the job load, the job concurrency, and the performance metric overall economic rate for the computing application; paragraph [0048]: FIG. 4 illustrates the correlation of the job load, the job concurrency, and the OER for the computing application in September; Note: The correlation of job load and job concurrency operating parameters with the OER metric relates to the target system objectives). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Jain by incorporating Chitalwala’s correlation metrics. The motivation would have been to monitor and optimize computing resource usage of a cloud-based computing application (Chitalwala, paragraph [0003]) by using correlation metrics to accurately quantify the relationship between current operating loads and target system objectives to trigger corrective provisioning actions..
Regarding claim 6, Jain discloses
wherein using the one or more machine learning models to output the one or more predicted actions comprises: identifying a plurality of predicted actions using the one or more metrics, the plurality of predicted actions comprising the one or more predicted actions, wherein using the one or more metrics to select the one or more predicted actions comprises predicting sets of one or more potential future states using the plurality of predicted actions, and selecting the one or more predicted actions for which a selected one of the sets was predicted that more closely matches the at least one desired state than at least one other of the sets (paragraph [0229]: In the event of deviations from the SLA parameters, the example reconfiguration managing circuitry ID5_C308 selects an alternate path (e.g., alternate graph) that exhibits predicted SLA compliance to a threshold margin; paragraph [0523]: The value network outputer ID6_526 predicts the value of the skilled agent when it is in a given state. In some examples the value network outputer ID6_526 predicts the value of the action the skilled agent can take. Predictions can be done to decide the state of the current prediction of the policy is. Predictions can be done to decide if it is advantageous to be in a certain state; paragraph [0608]: the quantization controller circuitry ID7_102 then determines whether the current iteration of the quantization strategy, current rewards and/or current performance results achieve a quantization topology that is improved (e.g., achieves an optimal solution (e.g., predefined optimal metrics) corresponding to performance thresholds; Note: Jain teaches identifying multiple candidate actions such as alternate paths or policies, predicting their future states and values such as predicted SLA compliance or the value of the action in a given state, and selecting the action or path that achieves the optimal solution or predicted SLA compliance, which corresponds to the desired state).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 2024/0007414, hereinafter Jain) in view of Raghunath et al. (US 2022/0116455, hereinafter Raghunath).
Regarding claim 14, Jain does not disclose wherein the one or more circuits comprise a data processing unit (“DPU”). Raghunath discloses wherein the one or more circuits comprise a data processing unit (“DPU”) (fig. 1-14; paragraph [0067]: the processor 604 may be embodied as a specialized x-processing unit (x-PU) also known as a data processing unit (DPU) … ). It would have been obvious to one of ordinary skill in the art at the time the claimed invention was effectively filed to incorporate Raghunath’s DPU into Jain’s processor. The motivation would have been to process one or more data streams and perform specific tasks and actions for the data streams (such as hosting microservices, performing service management or orchestration, organizing or managing server or data center hardware, managing service meshes, or collecting and distributing telemetry), outside of the CPU or general purpose processing hardware (Raghunath paragraph [0067]).
Allowable Subject Matter
Claims 5, 15, 17-19, 24-27, and 30 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Hari (US 2020/0073717) discloses “The system takes input on the budget goals, a service level agreement (SLA) that the application has to meet, incoming traffic throughput (measured in transactions per second (tps)) for the application, resource utilization metrics, thresholds, license costs associated with monitoring tools and third party frameworks, etc., and makes a determination on which resource to be scaled in a given scenario that meets the budget goals instead of creating a new container” (paragraph [0018]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISLEY N. KIM whose telephone number is (571)270-7832. The examiner can normally be reached M-F 11:30AM -7:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y. Blair can be reached on (571)270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SISLEY N KIM/Primary Examiner, Art Unit 2196 10/13/2026
